Faculty/Staff

Ramakrishna Govindu

Assistant Professor of Instruction
rgovindu@usf.edu
Campus: Sarasota-Manatee
Room: C229
Phone: 941-359-4523
Vita

Ram Govindu is an assistant professor of instruction in the School of Information Systems and Management, Muma College of Business, University of South Florida on the Sarasota-Manatee campus. He has a PhD degree in industrial engineering from Wayne State University; an M.Tech. degree with honors in quality, reliability & operations research from Indian Statistical Institute; and a B.E. degree with distinction in mechanical with specialization in production engineering from Osmania University. He was also an ASQ Certified Six Sigma Black Belt (CSSBB) and Certified Quality Engineer (CQE). Teaching, research, and consulting are his passion.

Govindu taught graduate/undergraduate courses at Wayne State University; designed/assisted courses in the Ford and Visteon Professional Engineering Management Master’s Program (EMMP); and conducted numerous need-based training programs in industry. His teaching interests include statistics and advanced analytics for business and engineering applications; operations research and decision sciences; Six Sigma, quality engineering and management; operations/supply chain management.

He has published his work in peer-reviewed international journals, peer-reviewed book chapters in handbooks, professional journals, and conference proceedings. He delivered several presentations at international conferences, gave invited talks in industry, and conducted numerous training programs for industry professionals. He has prepared several Small Business Innovation Research (SBIR) and other grant proposals. He has research interests in the areas of: operations research, decision support, and advanced analytics; multi agent, distributed, and intelligent agent systems; health care; operations/supply chain management.

Govindu has over 20 years of diverse and rich consulting, industry, and academic experience in the U.S. and India. He has developed and implemented algorithmic solutions for several hard business problems successfully saving organizations millions of dollars over the years. He has managed numerous projects, lead project teams, and executed consulting projects in industry. He wrote proposals in response to federal/state/local government RFPs, won numerous multi-year/multi-million-dollar government/commercial contracts, developed business leads and strategic partnerships, and helped a couple of small businesses grow.

Research

• Ngo F.T., Agarwal A., Govindu R., and MacDonald C. (2019). “Malicious Software Threats”. Chapter In: Holt T., Bossler A. (eds). The Palgrave Handbook of International Cybercrime and Cyberdeviance. Palgrave Macmillan, Cham. pp. 1-22. https://doi.org/10.1007/978-3-319-90307-1_35-1
• Ngo, Fawn T., Govindu, R., and Agarwal, A. (2018). “Traditional Regression Methods versus the Utility of Machine Learning Techniques in Forecasting Inmate Misconduct in the United States: An Exploration of the Prospects of the Techniques.” International Journal of Criminal Justice Sciences (IJCJS). July-Dec 2018, Vol. 13, No. 2, pp. 420-437. http://www.sascv.org/ijcjs/pdfs/NgoetalVol13Issue2IJCJS.pdf
• Agarwal, A., Govindu, R., Lodwig, S., and Ngo, F.T. (2016). “Solving the Jigsaw Puzzle: An Analytics Framework for Context Awareness in the Internet of Things” Issue on Data Analytics & Digital Technologies. Cutter Business Technology Journal, May 10, 2016, Vol. 29, No. 4, pp. 6-11. https://www.cutter.com/article/solving-jigsaw-puzzle-analytics-framework-context-awareness-internet-things-491266
• Ngo, Fawn T., Govindu, R., and Agarwal, A. (2015). “Assessing the Predictive Utility of Logistic Regression, Classification and Regression Tree, Chi-Squared Automatic Interaction Detection, and Neural Network Models in Predicting Inmate Misconduct.” American Journal of Criminal Justice. Vol. 40, No. 1, pp. 47-74. https://link.springer.com/article/10.1007/s12103-014-9246-6
• Govindu, R. and Chinnam R.B. (2010). “A software agent-component based framework for multi- agent supply chain modelling and simulation.” International Journal of Modelling and Simulation, Vol. 30, No. 2, pp. 155-171. https://www.tandfonline.com/doi/abs/10.1080/02286203.2010.11442569